Fixed pattern noise (“FPN”) is a component of the overall total noise in a candidate target detection and tracking system. For example, an infrared (“IR”) sensor on a missile can have FPN in addition to other noise. A high count of FPN reduces a detection sensor's sensitivity and hampers target tracking and identification due to a large noise component. In typical sensor applications, the standard deviation of FPN from a raw, uncorrected image can be as high as 300-400 counts. A count corresponds to the IR energy received at the IR detectors. A count can be proportional to the output voltage from the detectors for the received IR energy. For example, a weak potential target can have 10 to 20 counts. Temporal noise can be 1 to 2 counts, varying according to the outside temperature. A high count of FPN prevents detection of weak targets.
One purpose of any candidate target detection and tracking system is to identify candidate targets as early as possible. A non-uniformity correction (“NUC”) system reduces FPN to allow early target detection and reliable target tracking and recognition/identification. Traditional NUC systems seek to reduce the FPN to around or below the temporal noise (“TN”) level.
Known NUC systems incorporate a rotating “chopper-wheel,” or a blurring/deform lens, to separate the outside scene and the inside FPN on the focal plane array (“FPA”) of the sensor. A chopper wheel system rotates the blurring lens to remove the FPN from the total noise component. The chopper wheel system uses a motor to rotate the lens across the FPA. The hardware and software components to implement a chopper wheel with a lens and motor takes up space within the missile or detection device and adds complexity and cost. As detection systems get smaller, space can become a critical constraint on future designs for missiles, aircraft, and the like.
Other known NUC systems include scene-based NUC. Scene-based NUC systems use dithering to reduce the FPN. One-pixel level FPA dithering movement is difficult to control within scene-based NUC systems, and the rate of reducing the FPN is slow. Scene-based NUC systems can use a convergent median filter, but this change results in the rate of reducing the FPN being even slower.
Other NUC systems use estimated FPN components to remove the FPN from a received frame. Estimating FPN over the temperature ranges needed for today's applications and platforms is complex. Offset and gain values related to the received flux energy can vary greatly, over different temperature ranges. The estimation algorithms include high-order polynomials that result in complex processing loads on the detection sensor.
Removal or reduction of the FPN in a detection sensor is needed to maximize potential target identification, to detect targets early and to maintain tracking of an acquired target. Known NUC systems that estimate and reduce FPN, however, tend to be slow or not feasible due to their size.